Learning of Personalized Emotion Recognition Models Using Biological Signals in Daily Life

Abstract

In recent years, the widespread use of wearable sensors has facilitated the acquisition of biological signals, and this data have been used to learn emotion recognition models. However, due to the diversification and segmentation of emotion categories and the burden of subjective evaluation, collecting labels exhaustively is becoming difficult, and labeled instances may not be available in advance. When faced with unknown users for whom emotion label data are unavailable, conventional methods cannot effectively recognize emotions. Therefore, we propose a novel learning method for personalized emotion recognition models by introducing meta-learning using behavioral data of multiple people obtained in daily life, even if the unknown user's emotion-labeled data are not available. The results of applying the proposed method to the collected ECGs of several people during video viewing showed that the proposed method outperforms conventional supervised learning and zero-shot learning.